Abstract: This application provides an antenna apparatus and a communication method. The antenna apparatus includes a processing module and a plurality of transmission links. The processing module is configured to generate a to-be-transmitted signal, and transmit the to-be-transmitted signal to the plurality of transmission links. The plurality of transmission links are configured to transmit the to-be-transmitted signal. The plurality of transmission links include at least one electric near field transmission link and at least one magnetic near field transmission link. The electric near field transmission link includes an electric near field front end and an electric near field antenna. The electric near field front end is configured to transmit the to-be-transmitted signal to the electric near field antenna. The magnetic near field transmission link includes a magnetic near field front end and a magnetic near field antenna.

Abstract: Transfer learning (TL)-based systems, methods, and devices are provided for beam management in communication networks. In one aspect, a system may implement a transfer learning (TL)-based method comprising generating a neural network model for beam management in a telecommunications system, designating a plurality of labels, wherein one of the plurality of labels is associated with measurements from beams associated with a first set of measurements for a first frequency (f1). The system may also train the neural network model for the first frequency to produce a trained neural network model, including inputting measurements from beams associated with a second set of measurements for the first frequency (f1) and implement the trained neural network model to output a probability of each beam in the first set of measurements for the second frequency (f2) is a Top-1 beam, and determine beam identifiers (IDs) for the Top-K beams.

Abstract: A management system may receive a wireless signal transmitted to a receiver. The management system may determine, based on the wireless signal or a set of decoded bits associated with the wireless signal that were determined using an artificial intelligence (AI) receiver module of the receiver, whether a cyclic redundancy check (CRC) associated with the wireless signal is passed. The management system may perform, based on determining whether the CRC associated with the wireless signal is passed, at least one of: a data storage operation associated with the wireless signal, a fallback operation associated with the wireless signal, or an online label recovery operation associated with the wireless signal. The management system may perform, based on determining whether the CRC associated with the wireless signal is passed, a model modification operation to update a neural network (NN) of the AI receiver module of the receiver.

Abstract: Transfer learning (TL)-based systems, methods, and devices are provided for neural network and/or neuromorphic network transmitters/receivers with a set of desired modulation orders. In one aspect, a source system trains a full neural net modulation/demodulation model, from which one or more upper/output layers are removed, and the remaining base layers are transferred into a target system. A set of one or more upper/output layers are generated for the set of desired modulation orders, then transferred into, and trained in, the target system. The target system may store the transferred base layers and the trained set of one or more upper/output layers for the set of desired modulation orders, and use them to modulate/demodulate any transmission having one of the set of desired modulation orders.

Abstract: In some implementations, a base station testing system obtains model information associated with a neural network (NN) of an artificial intelligence (AI) receiver module of a receiver, training information associated with the NN; and the NN. The base station testing system determines one or more test cases for testing the NN and determines respective testing conditions associated with the one or more test cases. The base station testing system generates, based on the respective testing conditions, respective test case data associated with the one or more test cases. The base station testing system determines, based on testing the NN using the respective test case data associated with the one or more test cases, performance information associated with the NN, and generates, based on determining the performance information, a performance report associated with the NN.

Abstract: In some implementations, a network test device may generate a set of candidate points in a spatial frequency domain. The network test device may select, from the set of candidate points, an initial set of points in the spatial frequency domain that maximizes a minimum distance between pairs of points in the set of candidate points. The network test device may evaluate a metric for each point in the initial set of points. The network test device may adjust locations of one or more points in the initial set of points based on metrics associated with the one or more points, to obtain a final set of points in the spatial frequency domain. The network test device may create one or more channels based on the final set of points. The network test device may use the one or more channels to test a system in a simulation environment.

Abstract: A device may estimate targets and angle-of-arrival (AoA) and angle-of-departure (AoD) pairs for the targets based on a sensing-aware beam-forming (SABF) signal and a sensing-aware beam-nulling (SABN) signal received from another device. The device may perform post-processing of the AoA and AoD pairs to modify estimation accuracies for the AoA and AoD pairs, and may estimate path loss values for paths of the targets. The device may determine positions of the targets based on the AoA and AoD pairs, and may perform one or more actions based on the positions of the targets. The device may receive communication pilots from the other device, and may determine transmitter calibration coefficients for the other device and receiver calibration coefficients for the device based on the communication pilots.

Abstract: In some implementations, a network test device may identify a beam pattern associated with a gNodeB. The network test device may select, from the beam pattern, a first location, wherein the first location is associated with a first layer of a user equipment (UE). The network test device may select, based on the beam pattern, a second location associated with a second layer of the UE, wherein the second location is selected from a set of candidate locations associated with the first layer. The network test device may create one or more multiple-input multiple-output (MIMO) channels based on selected layers. The network test device may use the one or more MIMO channels to test a multiple user MIMO (MU-MIMO) system in a simulation or emulation environment.

Abstract: A base station testing system provides, for display, a graphical user interface (GUI) associated with facilitating testing of a base station by the base station testing system. The GUI includes one or more selectable indicators respectively associated with one or more modules of the GUI. The base station testing system obtains, based on providing the GUI, information indicating selection of a selectable indicator that is associated with analysis of beam patterns associated with the base station and thereby identifies a beam pattern analysis module that is associated with the selectable indicator. The base station testing system provides, for display, in a window of the GUI, information associated with the beam pattern analysis module, wherein the information associated with the beam pattern analysis module includes spatial frequency domain beam pattern analysis information associated with the base station.

Abstract: A base station testing system includes a plurality of user equipments (UEs), a UE adjustment component, and one or more processors. The one or more processors determine respective initial locations of the plurality of UEs with respect to a base station and cause the UE adjustment component to move the plurality of UEs to the respective initial locations. The one or more processors determine one or more candidate locations of each UE with respect to the base station and cause the UE adjustment component to move each UE to the one or more candidate locations of the UE. The one or more processors then determine respective optimal locations of the plurality of UEs with respect to the base station and cause the UE adjustment component to move the plurality of UEs to the respective optimal locations.

Abstract: In some implementations, a network test device may generate a set of candidate points in a spatial frequency domain. The network test device may select, from the set of candidate points, an initial set of points in the spatial frequency domain that maximizes a minimum distance between pairs of points in the set of candidate points. The network test device may evaluate a metric for each point in the initial set of points. The network test device may adjust locations of one or more points in the initial set of points based on metrics associated with the one or more points, to obtain a final set of points in the spatial frequency domain. The network test device may create one or more channels based on the final set of points. The network test device may use the one or more channels to test a system in a simulation environment.

Abstract: In some implementations, a wireless signal decoder may include a receiver configured to receive a wireless signal and convert the wireless signal into a digital signal. The wireless signal decoder may further include a processor configured to input the digital signal into a spiking neural network (SNN) and receive at least one predicted data symbol as output from the SNN. The at least one predicted data symbol may include a rate coded output or a latency coded output.

Abstract: A game server provides game data of a location-based game to a plurality of client devices based on a first set of one or more game designs. First activity data associated with the location-based game is received from the plurality of client devices based on the first set of one or more game designs. The game server determines whether the first activity data from the plurality of client devices meets a predetermined starting condition associated with a local virtual event. The local virtual event is started for the plurality of client devices in response to determining that the first activity data meets the predetermined starting condition. The game server provides game data based on the local virtual event to the plurality of client devices. The virtual event has a second game design that is different from the first set of one or more game designs.

Abstract: A device may estimate targets and angle-of-arrival (AoA) and angle-of-departure (AoD) pairs for the targets based on a sensing-aware beam-forming (SABF) signal and a sensing-aware beam-nulling (SABN) signal received from another device. The device may perform post-processing of the AoA and AoD pairs to modify estimation accuracies for the AoA and AoD pairs, and may estimate path loss values for paths of the targets. The device may determine positions of the targets based on the AoA and AoD pairs, and may perform one or more actions based on the positions of the targets. The device may receive communication pilots from the other device, and may determine transmitter calibration coefficients for the other device and receiver calibration coefficients for the device based on the communication pilots.

Abstract: A device may apply a precoder to input information symbols to generate delay-Doppler (DD) domain symbols, and may utilize an inverse symplectic finite Fourier transform to transform the DD domain symbols into a time-frequency (TF) domain signal. The device may utilize a Heisenberg transform to convert the TF domain signal to a time domain signal, and may add a cyclic prefix to the time domain signal to generate a modified time domain signal. The device may transmit the modified time domain signal to at least one user equipment.

Abstract: A game server provides game data of a location-based game to a plurality of client devices based on a first set of one or more game designs. First activity data associated with the location-based game is received from the plurality of client devices based on the first set of one or more game designs. The game server determines whether the first activity data from the plurality of client devices meets a predetermined starting condition associated with a local virtual event. The local virtual event is started for the plurality of client devices in response to determining that the first activity data meets the predetermined starting condition. The game server provides game data based on the local virtual event to the plurality of client devices. The virtual event has a second game design that is different from the first set of one or more game designs.

Abstract: In some implementations, a device may obtain information associated with one or more targets, wherein the information associated with the one or more targets includes sensing information and a communication performance control parameter. The device may determine a sensing beampattern based at least in part on the information associated with the one or more targets. The device may determine a target sensing autocorrelation matrix for the sensing beampattern. The device may identify a candidate sensing autocorrelation matrix for a joint communication and sensing transmission based at least in part on the target sensing autocorrelation matrix. The device may determine a target sensing precoder based at least in part on the candidate sensing autocorrelation matrix. The device may generate a joint communication and sensing precoder based at least in part on the target sensing precoder and the communication performance control parameter.

Abstract: In some implementations, a transmitter may determine that a difference between a total age of incorrect information (AoII) obtained in a current iteration of an iterative algorithm and a total AoII in a previous iteration of the iterative algorithm satisfies a tolerance factor for algorithm convergence. The transmitter may use the iterative algorithm to solve a convex optimization problem based on a plurality of iterations that continue until an objective function associated with AoII converges to within the tolerance factor for algorithm convergence. The transmitter may determine a precoder matrix and a vector of scheduling indicators from a solving of the convex optimization problem. The transmitter may transmit, using a downlink multi-user communication framework based on rate-splitting multiple access (RSMA) in a semantic-aware network, one or more updates to one or more receivers based on the precoder matrix and the vector of scheduling indicators.

Abstract: Hierarchical Power Management (HPM) architecture considers the limits of scaling on a power management controller, the autonomy at each die, and provides a unified view of the package to a platform. At a simplest level, HPM architecture has a supervisor and one or more supervisee power management units (PMUs) that communicate via at least two different communication fabrics. Each PMU can behave as a supervisor for a number of supervisee PMUs in a particular domain. HPM addresses these needs for products that comprise a collection of dice with varying levels of power and thermal management capabilities and needs. HPM serves as a unified mechanism than can span collection of dice of varying capability and function, which together form a traditional system-on-chip (SoC). HPM provides a basis for managing power and thermals across a diverse set of dice.

Abstract: A device may determine whether to utilize perfect channel state information at transmitter (CSIT) or imperfect CSIT, and may calculate, based on determining to utilize the perfect CSIT, a first power allocation, a first rate allocation, and a first precoder allocation for a private stream of a user device. The device may determine first parameters or second parameters for the first power allocation, the first rate allocation, and the first precoder allocation, and may generate a first transmit signal and a first data allocation based on the first power allocation, the first rate allocation, the first precoder allocation, and the first parameters or the second parameters. The device may provide the first transmit signal, with the first data allocation, to the user device via the private stream.